Analysis of the child and adolescent needs and strengths assessment in a First Nation population
Kowatch, Kristy R.
Master of Arts
DisciplinePsychology : Clinical
SubjectSocial determinants of health
Mental health needs in First Nations' children
Mental wellness services
MetadataShow full item record
First Nations youth are one of the fastest growing demographics in Canada, yet they are more likely to experience adverse health and life circumstances than non-Indigenous Canadians. Developing and implementing appropriate interventions for mental health is a priority area in decreasing this health gap, and requires the incorporation of First Nation models of mental wellness. Mental Wellness for First Nations’ youth is tied to interpersonal and cultural factors such as relationships with caregivers and the greater community, caregiver and/or community access to necessary resources, and cultural identities. Examining these wider sociocultural factors, in combination with youth characteristics and strengths, provides a more comprehensive understanding of how to address mental health needs in First Nation communities. Working in collaboration with a First Nation based community health provider, the Child and Adolescents Needs and Strengths (CANS) assessment was analyzed for 178 First Nation children to identify specific mental health intervention needs and explore predictors of mental health needs. The CANS is a reliable measure that assesses youth mental health needs, caregiver needs, individual strengths, environmental strengths, as well as many other factors. The most commonly reported mental health intervention needs were seen for Anxiety, Mood, Emotional Control, and Adjustment to Trauma. Hierarchical regression identified referents’ age, sex, Functioning, Individual Strengths, and Family/Caregiver Needs and Strengths domain scores as predictive of mental health intervention needs. Age and Functioning domain scores were robust individual predictors of mental health needs across most models, yet sex was not individually predictive in any model.